Enhanced De la Garza routing algorithm for wireless sensor networks

Large scale wireless sensor networks bring up many challenges in efficient and effect routing algorithm design due to the complexity and hardware constraints. The scalability challenge may be mitigated from a macroscopic perspective. One example is the distributed De la Garza iteration (DDLGI) algorithm for global routing load-balancing, based on a set of partial differential equations (PDEs) iteratively solved by the De la Garza method. Although DDLGI always converges to the numerical solution of the PDEs, the convergence may be unacceptably slow, which leads to excess energy consumption in communication. Thus, we propose the enhanced De la Garza routing (E-DLGR) algorithms in which nodes may exchange less information and only need to exchange these information with closer nodes for iteration. In addition, E-DLGR may converge faster and further reduce energy consumption without too much sacrifice of accuracy. The simulation results are presented to assess the efficiency and effectiveness of E-DLGR in energy savings.